Aug 2026· Buildings· Vol 16, pp. 3270· 0 citations· 37 references
TL;DR
An interpretable machine learning framework to predict the compressive strength of concrete incorporating blast-furnace slag (BFS) and a conceptual graphical user interface (GUI) is proposed to bridge the gap between theoretical models and future batch-plant deployment.
Abstract
This study develops an interpretable machine learning framework to predict the compressive strength of concrete incorporating blast-furnace slag (BFS). To address the critical issue of data leakage prevalent in conventional random splitting, a rigorous grouped validation strategy, specifically the GroupKFold algorithm, was implemented based on unique mixture proportions. Seven machine learning algorithms, including linear baselines and tree-based ensembles, were comprehensively evaluated. Results indicate that the XGBoost model achieved the highest predictive accuracy and stability, yielding a mean Root Mean Squared Error (RMSE) of 5.39 MPa and a minimal standard deviation of 0.54 MPa. A multi-criteria decision-making (MCDM) approach mathematically confirmed XGBoost as the optimal model. Furthermore, SHapley Additive exPlanations (SHAP) combined with data-density rug plots were utilized to uncover the non-linear interactions between BFS and other components. Rather than asserting direct causality, the SHAP analysis provides robust model-based associations that align with macroscopic physical expectations while strictly preventing over-interpretation in sparse data regions. Finally, a conceptual graphical user interface (GUI) is proposed to bridge the gap between theoretical models and future batch-plant deployment. This research balances rigorous high-precision prediction with transparent interpretability for BFS concrete design.
An interpretable machine-learning framework for predicting the splitting strength of asphalt concrete and supporting data-driven mixture design and a GUI platform integrating prediction and SHAP-based explanation was developed to improve the accessibility and practical applicability of the proposed framework.
J. Xing, Xiao Tan, Dongzhan Jin et al.· 0 citations
Compressive strength is the single most important design parameter governing the safety, serviceability, and economy of concrete structures, yet its determination through standard 7-, 14-, or 28-day destructive cylinder/cube testing is slow, costly, and unable to assess concrete already cast in place. This study develops and evaluates a Random Forest (RF) regression model to predict the compressive strength of concrete directly from eight standard mix-design parameters — cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and curing age — using Yeh's (1998) benchmark dataset of 1,030 experimentally tested concrete mixtures. Following data cleaning, exploratory correlation analysis, an 80:20 train-test split, and five-fold GridSearchCV hyperparameter tuning, the optimized Random Forest model is benchmarked against Linear Regression, Ridge Regression, and Support Vector Regression using the coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The Random Forest model achieves the strongest predictive performance of the models tested, substantially outperforming the linear baselines and confirming that concrete strength development is governed by non-linear interactions among mix constituents. Feature importance analysis further shows that curing age and cement content are the dominant predictors, while water content exerts a clear negative influence consistent with Abrams' Law, and coarse/fine aggregates contribute comparatively little, consistent with their role as largely inert fillers. These findings demonstrate that Random Forest regression offers a fast, accurate, and interpretable, non-destructive alternative to conventional strength testing, with practical value for mix-design optimization, quality control, and early-stage structural decision-making.
M. Selvakumar, S. Geetha, P. K. Kumar et al.· International journal of com...· 0 citations
An interpretable machine learning framework integrating Extreme Gradient Boosting with Shapley Additive Explanations to predict the 28-day compressive strength of fly ash-based geopolymer concrete (FA-GPC) is developed and experimentally validates.
X. Shi, Haoxiang Hu, Zhenhua Duan et al.· 0 citations
5Cr-0.5Mo ferritic steels are widely used in high-temperature power-plant components. Although artificial neural network (ANN) models have shown good performance in predicting tensile properties, they provide limited insight into predictions and generally do not quantify the uncertainty. In this study, three tree-based machine learning models—Random Forest (RF), XGBoost (XGB), and Gradient Boosting (GB)—were developed using 36 unique alloy grade–temperature observations from a validated NIMS 5Cr-0.5Mo tensile dataset. The model performance was evaluated using leave-one-grade-out (LOGO) cross-validation, with pooled out-of-fold (OOF) predictions used to assess the overall performance. SHapley Additive exPlanations (SHAP) were used to examine feature contributions, whereas Gaussian Process Regression (GPR) was evaluated as a proof-of-concept for uncertainty quantification of yield strength (YS). GB showed the strongest performance for ultimate tensile strength (UTS) and reduction in area (RA), achieving pooled OOF R2 values of 0.9698 and 0.9570, respectively. RF achieved corresponding R2 values of 0.9406 and 0.9488, respectively. SHAP identified the test temperature as the most influential feature across all four properties, whereas the Cr content and austenite grain size contributed significantly to the strength predictions. For YS, the GPR achieved complete empirical coverage of the 95% predictive intervals, although the relatively large mean interval width indicated conservative uncertainty estimates. Given the limited dataset and feature correlations, the SHAP results should be regarded as exploratory, rather than mechanistic. Overall, this study demonstrates the potential of interpretable, uncertainty-aware ML for small alloy datasets, while emphasizing the need for larger, compositionally diverse datasets and independent validation.
A comparative framework evaluating nine regression algorithms using the UCI Concrete Compressive Strength dataset, jointly integrating correlation-corrected statistical validation, multi-model Bayesian optimization, and domain-informed feature engineering with SHAP interpretation, rarely combined in prior concrete-strength studies.
Musthafa 'Abduh Fakhruddin, Sri Winarno, Acun Kardianawati· IDEALIS : InDonEsiA journaL...· 0 citations
The novel compression-cast concrete (CCC) delivers superior mechanical and durability performance over conventional vibration-cast concrete (VCC), alongside economic and environmental advantages. However, its widespread adoption requires an optimized and systematic design method. This study presents a data-driven framework that integrates machine learning (ML) and multiobjective optimization for both forward prediction and inverse design of CCC. Using an experimental data set, various ML models were trained, with Optuna-optimized backpropagation neural networks (OP_BPNN) showing the best accuracy. Model interpretability was enhanced using individual conditional expectation and Shapley additive explanations. The validated OP_BPNN served as a surrogate in inverse optimization via nondominated sorting genetic algorithm III (NSGA-III), targeting compressive strength while minimizing cost and
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emissions and maximizing density. Optimal solutions were ranked using the technique for order of preference by similarity to ideal solution (TOPSIS). Compared to VCC, the optimized CCC showed up to 15% potential reduction in cost and 38% lower
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emissions, as predicted by the model within the studied parameter range. A user-friendly graphical interface was developed to facilitate practical implementation. The framework offers a scalable tool for CCC design aligned with project-specific performance and sustainability goals.
M. Tahir, Yingwu Zhou, Biao Hu et al.· Journal of materials in civi...· 0 citations
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